Segmentation of Brain Tumors using Meta Heuristic Algorithms

Christ, J.; Sivagowri, S.; Babu, G. Ravindra

doi:10.15764/cs.2014.01001

Cited by 14 publications

(4 citation statements)

References 9 publications

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“…A wide overview of bio-inspired optimization algorithms [6], grouped by the various biological fields that inspired each and the areas where these algorithms have been most successfully applied. Almost all the algorithms execute with heuristic population-based search processes that combine random variation and selection.…”

Section: Optimization Techniquesmentioning

confidence: 99%

An Optimized Approach for Image Segmentation on Mobile Devices

Turkar¹

2021

TURCOMAT

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Now a day’s image segmentation is widely used in many multimedia applications. We have introduced the optimized approach for image segmentation based on clustering for use on smart devices. The proposed optimized approach is based on the combination of partitioning of images using quad-tree and Ant Colony Optimization. This approach utilizes the strong ability of ACO i.e global optimization. The proposed optimized algorithm is evaluated on images of standard data set and its performance is compared with existing clustering algorithms. The qualitative and quantitative analysis has been performed to measure the efficacy of the optimized approach over conventional existing algorithms. This procedure obtains better quality results than existing clustering algorithms.

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Section: Optimization Techniquesmentioning

confidence: 99%

An Optimized Approach for Image Segmentation on Mobile Devices

Turkar¹

2021

TURCOMAT

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“…Computer vision-based applications of biomedical imaging are gaining more importance as they provide recognition information to the radiologist for batter treatment-related problems. Different medical imaging techniques and methods that include X-ray, Magnetic Resonance Imaging (MRIs), Ultrasound, and Computed Tomography (CT), have a great influence on the diagnosis and treatment process of patients [2,3].…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges

et al. 2020

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Deep Learning (DL) algorithms enabled computational models consist of multiple processing layers that represent data with multiple levels of abstraction. In recent years, usage of deep learning is rapidly proliferating in almost every domain, especially in medical image processing, medical image analysis, and bioinformatics. Consequently, deep learning has dramatically changed and improved the means of recognition, prediction, and diagnosis effectively in numerous areas of healthcare such as pathology, brain tumor, lung cancer, abdomen, cardiac, and retina. Considering the wide range of applications of deep learning, the objective of this article is to review major deep learning concepts pertinent to brain tumor analysis (e.g., segmentation, classification, prediction, evaluation.). A review conducted by summarizing a large number of scientific contributions to the field (i.e., deep learning in brain tumor analysis) is presented in this study. A coherent taxonomy of research landscape from the literature has also been mapped, and the major aspects of this emerging field have been discussed and analyzed. A critical discussion section to show the limitations of deep learning techniques has been included at the end to elaborate open research challenges and directions for future work in this emergent area.

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“…E-health care systems are beneficial in various medical domains [ 1 ]. Since more computer vision-based biomedical imaging application has gained more importance because these applications provide recognizable information to the radiologists for better treatment [ 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

Bone Age Assessment Empowered with Deep Learning: A Survey, Open Research Challenges and Future Directions

et al. 2020

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Deep learning is a quite useful and proliferating technique of machine learning. Various applications, such as medical images analysis, medical images processing, text understanding, and speech recognition, have been using deep learning, and it has been providing rather promising results. Both supervised and unsupervised approaches are being used to extract and learn features as well as for the multi-level representation of pattern recognition and classification. Hence, the way of prediction, recognition, and diagnosis in various domains of healthcare including the abdomen, lung cancer, brain tumor, skeletal bone age assessment, and so on, have been transformed and improved significantly by deep learning. By considering a wide range of deep-learning applications, the main aim of this paper is to present a detailed survey on emerging research of deep-learning models for bone age assessment (e.g., segmentation, prediction, and classification). An enormous number of scientific research publications related to bone age assessment using deep learning are explored, studied, and presented in this survey. Furthermore, the emerging trends of this research domain have been analyzed and discussed. Finally, a critical discussion section on the limitations of deep-learning models has been presented. Open research challenges and future directions in this promising area have been included as well.

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Segmentation of Brain Tumors using Meta Heuristic Algorithms

Cited by 14 publications

References 9 publications

An Optimized Approach for Image Segmentation on Mobile Devices

An Optimized Approach for Image Segmentation on Mobile Devices

Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges

Bone Age Assessment Empowered with Deep Learning: A Survey, Open Research Challenges and Future Directions

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